code: def check_finite_loss(loss: Optional[Tensor]) -> None:
      """Checks for finite loss value.
  
      Args:
          loss: the loss value to check to be finite
  
      """
      if loss is not None and not torch.isfinite(loss).all():
          raise ValueError(
              f"The loss returned in `training_step` is {loss}."
          )
  
imports: import inspect
  from contextlib import contextmanager
  from typing import Any, Callable, ContextManager, Generator, Optional, Tuple, Type
  import torch
  import torch.distributed
  from torch import Tensor
  import lightning.pytorch
  from lightning.fabric.utilities.distributed import _distributed_is_initialized
  from lightning.fabric.utilities.imports import _TORCH_EQUAL_2_0, _TORCH_GREATER_EQUAL_1_13
  from lightning.fabric.utilities.warnings import PossibleUserWarning
  from lightning.pytorch.accelerators.xla import XLAAccelerator
  from lightning.pytorch.callbacks.timer import Timer
  from lightning.pytorch.loops import _Loop
  from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher, _PrefetchDataFetcher
  from lightning.pytorch.loops.progress import _BaseProgress
  from lightning.pytorch.strategies import FSDPStrategy
  from lightning.pytorch.strategies.parallel import ParallelStrategy
  from lightning.pytorch.strategies.strategy import Strategy
  from lightning.pytorch.trainer.states import RunningStage
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
label_desc: Use image node_id 1 for calling a global function with example usage: check_finite_loss(loss) without return types